An Enhanced CNN Framework for Robust Brain Tumor Detection from MRI Scans using impact features attenuation

Mohanaed Ajmi Falih

doi:10.71229/5pnv3m24

Authors

Mohanaed Ajmi Falih Babylon Education,Ministry of Education

DOI:

https://doi.org/10.71229/5pnv3m24

Keywords:

Brain Tumour,, Impact Features Attention, , Deep Learning,, Medical Image Classification, , Convolutional Neural Network

Abstract

The human brain serves as the central processing unit of the body, and abnormal cellular proliferation within the brain can lead to tumourigenesis, often culminating in malignancies such as brain cancer. In the domain of medical imaging, Computer Vision (CV) plays a pivotal role by providing objective, data-driven insights that minimize the impact of human cognitive biases. This study focuses on utilizing enhance Convolutional Neural Network (CNN) techniques for the automated detection of brain tumors from Magnetic Resonance Imaging (MRI) scans, with Impact Features Attention (IFA) .The methodology normalize the MRI image then extract the full features from, followed by classifying the them, and apply Convolutional Neural Network (CNN) segmentation to accurately delineate tumor regions. The model's robustness is ensured through extensive training, testing, and validation using three datasets. Key performance metrics, including accuracy, sensitivity, specificity, precision, recall, and F1 score, are utilized to evaluate the model's diagnostic capability, with the goal of outperforming existing methods. The proposed model achieves when benchmark datasets (Br35H, FIGSHARE, SARTAJ) achieving 96.91% as accuracy , recall of 96%, and an F1 score of 96.8%, demonstrating its superior efficacy in brain tumor detection.

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